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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- indonlu |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: indobert-distilled-optimized-for-classification |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: indonlu |
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type: indonlu |
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args: smsa |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9023809523809524 |
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- name: F1 |
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type: f1 |
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value: 0.9020516403647337 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# indobert-distilled-optimized-for-classification |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the indonlu dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5991 |
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- Accuracy: 0.9024 |
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- F1: 0.9021 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5.262995179171344e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 33 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| 1.2938 | 1.0 | 688 | 0.8433 | 0.8484 | 0.8513 | |
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| 0.711 | 2.0 | 1376 | 0.6408 | 0.8881 | 0.8878 | |
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| 0.4416 | 3.0 | 2064 | 0.7964 | 0.8794 | 0.8793 | |
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| 0.2907 | 4.0 | 2752 | 0.7559 | 0.8897 | 0.8900 | |
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| 0.2065 | 5.0 | 3440 | 0.6892 | 0.8968 | 0.8974 | |
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| 0.1574 | 6.0 | 4128 | 0.6881 | 0.8913 | 0.8906 | |
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| 0.1131 | 7.0 | 4816 | 0.6224 | 0.8984 | 0.8982 | |
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| 0.0865 | 8.0 | 5504 | 0.6312 | 0.8976 | 0.8970 | |
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| 0.0678 | 9.0 | 6192 | 0.6187 | 0.8992 | 0.8989 | |
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| 0.0526 | 10.0 | 6880 | 0.5991 | 0.9024 | 0.9021 | |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.0+cu111 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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